Spectral factorization in periodically time-varying systems and application to navigation problems.
Spectral factorization has been used previously to derive the steady-state solution of Kalman filtering equations without iteration for constant coefficient systems. The present work extends the spectral factorization algorithm to time-varying systems having periodic coefficient matrices for cases of both discrete and continuous systems. Time-consuming, expensive iterations of sequential covariance equations are not required to reach the final solution since this is an algebraic algorithm employing existing eigenvalue, eigenvector subroutines. The computer program incorporating the algorithm is suitable for sensitivity studies in formulating navigation and guidance strategies of low-thrust interplanetary missions. The determination of an optimum tracking pattern from an earth station is examined as an example.